Source: photo taken May 2023 at the Department for Transport’s Data Science for Transport conference
Premises
There are often trade-offs between level of detail and simplicity in transport models.
Simplicity has advantages: transparency, reproducibility, flexibility, number of scenarios that can be run, and explainability.
Results at the route segment level are the most useful output of transport models for many applications, including active travel planning.
For models to be future-proof, they need to be easy for others to understand, reproduce, and extend.
The 4-stage model
A transport models from first principles can be expressed in 4 stages, according the classic four-step model:
Trip generation
Trip distribution
Mode choice
Route assignment
The 4-stage model as a DAG
graph TD
A[Trip Generation] --> B[Trip Distribution]
B --> C[Mode Choice]
C --> D[Route Assignment]
Figure 1: The 4-stage transport modelling framework presented as a linear Directed Acyclic Graph (DAG)
A more realistic model?
The dependency structure may be more realistic, with trip generation, distribution and mode choice all affected by the network.
graph TD
E[Network] --> D[Route Assignment: s4]
F[Other Factors] --> C
F --> B
C --> D
D --> B
E --> B[Trip Distribution: s2]
E --> C[Mode Choice: s3]
B --> C
B --> A[Trip Generation: s1]
Figure 2: A 6-stage transport modelling framework with recursive dependencies between Trip Distribution and Route Assignment stages.
Why the 4-stage model?
Simplicity
Lack of data
Lack of methods
Epistemic bias
We should not throw the baby out with the bathwater, but reform, rebuild or retrofit transport modelling for the 21st Century.
So let’s see what we can do with the 4-stage model before moving on to more complex models.
Schematic diagram illustrating the modelling process, geographic analysis and the four-stage model in the context of the wider transport planning process Lovelace (2021), building on Ortúzar S. and Willumsen (2011).
